Abstract
Increasing desertification rates have adversely affected biodiversity and ecosystem functioning in sandy lands. Ecological restoration is an effective way to combat desertification. Specific microtopographic characteristics may facilitate vegetation growth, enhancing the success of restoration efforts. However, limited research to date has explored how microtopography may guide the “precision restoration” of sandy lands by supporting spatially continuous patterns of vegetation growth. Here, high-resolution unmanned aerial vehicle (UAV) multispectral data were used to identify individual species and extract microtopographic variables, and the relationships between vegetation growth and microtopography were characterized for the Hunshandake Sandy Land in China. The distribution of three dominant shrub species and grasses was investigated by comparing the performance of five popular machine-learning methods. An auto-marking watershed algorithm was then developed to discriminate individual semi-shrubs (Artemisia desertorum). Finally, a new vegetation growth index (VGI), calculated from the UAV-derived crown area, normalized difference vegetation index (NDVI), and canopy height, was used to characterize the relationships between vegetation growth and several microtopographic variables (aspect, slope, and a topographic wetness index [TWI]). With the highest species classification accuracy (94.36%) and individual discrimination rate (81%), areas with high humidity (TWI), gentle and shady slopes were found to most strongly support vegetation growth; for grasses, VGI was lower in artificially-restored regions than naturally-developed regions with similar microtopographic characteristics. These findings provide valuable guidance on the use of UAV to support diverse ecological restoration solutions in sandy lands with a “precision restoration” strategy, thereby improving the survival of key vegetation for sand restoration, especially in remote areas.
Published Version
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